Construction of a Combined Hypoxia-related Genes Model for Hepatocellular Carcinoma Prognosis.

IF 1.5 4区医学 Q4 CHEMISTRY, MEDICINAL Current computer-aided drug design Pub Date : 2023-01-01 DOI:10.2174/1573409919666221223123610

Liping Ren, Xianrun Pan, Lin Ning, Di Gong, Jian Huang, Kejun Deng, Lei Xie, Yang Zhang

{"title":"Construction of a Combined Hypoxia-related Genes Model for Hepatocellular Carcinoma Prognosis.","authors":"Liping Ren, Xianrun Pan, Lin Ning, Di Gong, Jian Huang, Kejun Deng, Lei Xie, Yang Zhang","doi":"10.2174/1573409919666221223123610","DOIUrl":null,"url":null,"abstract":"Background: Hepatocellular carcinoma (HCC) is the most common liver malignancy where tumorigenesis and metastasis are believed to be tied to the hallmarks of hypoxia and tumor microenvironment (TME).Methods: In this study, to investigate the relationships among hypoxia, TME, and HCC prognosis, we collected two independent datasets from a public database (TCGA-LIHC for identification, GSE14520 for validation) and identified the hypoxia-related differentially expressed genes (DEGs) from the TCGA data, and the univariable Cox regression and lasso regression analyses were performed to construct the prognosis model. An HCC prognosis model with 4 hypoxiarelated DEGs (\"NDRG1\", \"ENO1\", \"SERPINE1\", \"ANXA2\") was constructed, and high- and low-risk groups of HCC were established by the median of the model risk score.Results: The survival analysis revealed significant differences between the two groups in both datasets, with the results of the AUC of the ROC curve of 1, 3, and 5 years in two datasets indicating the robustness of the prognosis model. Meanwhile, for the TCGA-LIHC data, the immune characteristics between the two groups revealed that the low-risk group presented higher levels of activated NK cells, monocytes, and M2 macrophages, and 7 immune checkpoint genes were found upregulated in the high-risk group. Additionally, the two groups have no difference in molecular characteristics (tumor mutational burden, TMB). The proportion of recurrence was higher in the high-risk group, and the correlation between the recurrence month and risk score was negative, indicating high-risk correlates with a short recurrence month.Conclusion: In summary, this study shows the association among hypoxic signals, TME, and HCC prognosis and may help reveal potential regulatory mechanisms between hypoxia, tumorigenesis, and metastasis in HCC. The hypoxia-related model demonstrated the potential to be a predictor and drug target of prognosis.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"19 2","pages":"150-161"},"PeriodicalIF":1.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current computer-aided drug design","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/1573409919666221223123610","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}

引用次数: 1

Abstract

Background: Hepatocellular carcinoma (HCC) is the most common liver malignancy where tumorigenesis and metastasis are believed to be tied to the hallmarks of hypoxia and tumor microenvironment (TME).

Methods: In this study, to investigate the relationships among hypoxia, TME, and HCC prognosis, we collected two independent datasets from a public database (TCGA-LIHC for identification, GSE14520 for validation) and identified the hypoxia-related differentially expressed genes (DEGs) from the TCGA data, and the univariable Cox regression and lasso regression analyses were performed to construct the prognosis model. An HCC prognosis model with 4 hypoxiarelated DEGs ("NDRG1", "ENO1", "SERPINE1", "ANXA2") was constructed, and high- and low-risk groups of HCC were established by the median of the model risk score.

Results: The survival analysis revealed significant differences between the two groups in both datasets, with the results of the AUC of the ROC curve of 1, 3, and 5 years in two datasets indicating the robustness of the prognosis model. Meanwhile, for the TCGA-LIHC data, the immune characteristics between the two groups revealed that the low-risk group presented higher levels of activated NK cells, monocytes, and M2 macrophages, and 7 immune checkpoint genes were found upregulated in the high-risk group. Additionally, the two groups have no difference in molecular characteristics (tumor mutational burden, TMB). The proportion of recurrence was higher in the high-risk group, and the correlation between the recurrence month and risk score was negative, indicating high-risk correlates with a short recurrence month.

Conclusion: In summary, this study shows the association among hypoxic signals, TME, and HCC prognosis and may help reveal potential regulatory mechanisms between hypoxia, tumorigenesis, and metastasis in HCC. The hypoxia-related model demonstrated the potential to be a predictor and drug target of prognosis.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

肝细胞癌预后缺氧相关基因联合模型的构建

背景:肝细胞癌(HCC)是最常见的肝脏恶性肿瘤，其肿瘤发生和转移被认为与缺氧和肿瘤微环境(TME)有关。方法:本研究为探讨缺氧、TME与HCC预后之间的关系，我们从公共数据库(TCGA- lihc进行鉴定，GSE14520进行验证)中收集两个独立的数据集，从TCGA数据中鉴定缺氧相关的差异表达基因(DEGs)，并进行单变量Cox回归和lasso回归分析，构建预后模型。构建含“NDRG1”、“ENO1”、“SERPINE1”、“ANXA2”4个缺氧相关基因的HCC预后模型，按模型风险评分中位数划分HCC高危组和低危组。结果:生存分析显示两组患者在两个数据集上存在显著差异，ROC曲线1年、3年和5年的AUC结果表明预后模型的稳健性。同时，在TCGA-LIHC数据中，两组之间的免疫特征显示，低危组的活化NK细胞、单核细胞和M2巨噬细胞水平较高，高危组有7个免疫检查点基因上调。此外，两组在分子特征(肿瘤突变负荷，TMB)上没有差异。高危组复发比例较高，复发月份与风险评分呈负相关，提示复发月份短与高危相关。结论:综上所述，本研究显示了缺氧信号、TME与HCC预后之间的关联，并可能有助于揭示HCC中缺氧、肿瘤发生和转移之间的潜在调控机制。低氧相关模型显示了作为预后预测因子和药物靶点的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Current computer-aided drug design 医学-计算机：跨学科应用

CiteScore

3.70

自引率

5.90%

发文量

审稿时长

>12 weeks

期刊介绍： Aims & Scope Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.